International Journal of Approximate Reasoning最新文献

Incremental attribute reduction with α,β-level intuitionistic fuzzy sets 用 α、β 级直观模糊集递减属性

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2024-11-14 DOI: 10.1016/j.ijar.2024.109326

Pham Viet Anh , Nguyen Ngoc Thuy , Le Hoang Son , Tran Hung Cuong , Nguyen Long Giang

The intuitionistic fuzzy set theory is recognized as an effective approach for attribute reduction in decision information systems containing numerical or continuous data, particularly in cases of noisy data. However, this approach involves complex computations due to the participation of both the membership and non-membership functions, making it less feasible for data tables with a large number of objects. Additionally, in some practical scenarios, dynamic data tables may change in the number of objects, such as the addition or removal of objects. To overcome these challenges, we propose a novel and efficient incremental attribute reduction method based on

α, β

-level intuitionistic fuzzy sets. Specifically, we first utilize the key properties of

α, β

-level intuitionistic fuzzy sets to construct a distance measure between two

α, β

-level intuitionistic fuzzy partitions. This extension of the intuitionistic fuzzy set model helps reduce noise in the data and shrink the computational space. Subsequently, we define a new reduct and design an efficient algorithm to identify an attribute subset in fixed decision tables. For dynamic decision tables, we develop two incremental calculation formulas based on the distance measure between two

α, β

-level intuitionistic fuzzy partitions to improve processing time. Accordingly, some important properties of the distance measures are also clarified. Finally, we design two incremental attribute reduction algorithms that handle the addition and removal of objects. Experimental results have demonstrated that our method is more effective than incremental methods based on fuzzy rough set and intuitionistic fuzzy set approaches in terms of execution time and classification accuracy from the obtained reduct.

在包含数值或连续数据的决策信息系统中，直觉模糊集理论被认为是一种有效的属性还原方法，尤其是在有噪声数据的情况下。然而，由于成员和非成员函数的参与，这种方法涉及复杂的计算，因此对于对象数量较多的数据表来说不太可行。此外，在某些实际场景中，动态数据表的对象数量可能会发生变化，例如对象的添加或删除。为了克服这些挑战，我们提出了一种基于 α、β 级直觉模糊集的新型高效增量属性缩减方法。具体来说，我们首先利用 α,β 级直觉模糊集的关键属性来构建两个 α,β 级直觉模糊分区之间的距离度量。对直观模糊集模型的这一扩展有助于减少数据中的噪声，缩小计算空间。随后，我们定义了一种新的还原法，并设计了一种高效算法来识别固定决策表中的属性子集。对于动态决策表，我们根据两个 α、β 级直觉模糊分区之间的距离度量开发了两个增量计算公式，以缩短处理时间。相应地，我们还阐明了距离度量的一些重要属性。最后，我们设计了两种增量属性缩减算法来处理对象的添加和删除。实验结果表明，与基于模糊粗糙集和直观模糊集方法的增量方法相比，我们的方法在执行时间和从获得的还原中分类的准确性方面更有效。

{"title":"Incremental attribute reduction with α,β-level intuitionistic fuzzy sets","authors":"Pham Viet Anh , Nguyen Ngoc Thuy , Le Hoang Son , Tran Hung Cuong , Nguyen Long Giang","doi":"10.1016/j.ijar.2024.109326","DOIUrl":"10.1016/j.ijar.2024.109326","url":null,"abstract":"<div><div>The intuitionistic fuzzy set theory is recognized as an effective approach for attribute reduction in decision information systems containing numerical or continuous data, particularly in cases of noisy data. However, this approach involves complex computations due to the participation of both the membership and non-membership functions, making it less feasible for data tables with a large number of objects. Additionally, in some practical scenarios, dynamic data tables may change in the number of objects, such as the addition or removal of objects. To overcome these challenges, we propose a novel and efficient incremental attribute reduction method based on <span><math><mi>α</mi><mo>,</mo><mi>β</mi></math></span>-level intuitionistic fuzzy sets. Specifically, we first utilize the key properties of <span><math><mi>α</mi><mo>,</mo><mi>β</mi></math></span>-level intuitionistic fuzzy sets to construct a distance measure between two <span><math><mi>α</mi><mo>,</mo><mi>β</mi></math></span>-level intuitionistic fuzzy partitions. This extension of the intuitionistic fuzzy set model helps reduce noise in the data and shrink the computational space. Subsequently, we define a new reduct and design an efficient algorithm to identify an attribute subset in fixed decision tables. For dynamic decision tables, we develop two incremental calculation formulas based on the distance measure between two <span><math><mi>α</mi><mo>,</mo><mi>β</mi></math></span>-level intuitionistic fuzzy partitions to improve processing time. Accordingly, some important properties of the distance measures are also clarified. Finally, we design two incremental attribute reduction algorithms that handle the addition and removal of objects. Experimental results have demonstrated that our method is more effective than incremental methods based on fuzzy rough set and intuitionistic fuzzy set approaches in terms of execution time and classification accuracy from the obtained reduct.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109326"},"PeriodicalIF":3.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy centrality measures in social network analysis: Theory and application in a university department collaboration network 社会网络分析中的模糊中心度量：大学院系协作网络中的理论与应用

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2024-11-13 DOI: 10.1016/j.ijar.2024.109319

Annamaria Porreca , Fabrizio Maturo , Viviana Ventre

The motivation behind this research stems from the inherent complexity and vagueness in human social interactions, which traditional Social Network Analysis (SNA) approaches often fail to capture adequately. Conventional SNA methods typically represent relationships as binary or weighted ties, thereby losing the subtle nuances and inherent uncertainty in real-world social connections. The need to preserve the vagueness of social relations and provide a more accurate representation of these relationships motivates the introduction of a fuzzy-based approach to SNA. This paper proposes a novel framework for Fuzzy Social Network Analysis (FSNA), which extends traditional SNA to accommodate the vagueness of relationships. The proposed method redefines the ties between nodes as fuzzy numbers rather than crisp values and introduces a comprehensive set of fuzzy centrality indices, including fuzzy degree centrality, fuzzy betweenness centrality, and fuzzy closeness centrality, among others. These indices are designed to measure the importance and influence of nodes within a network while preserving the uncertainty in the relationships between them. The applicability of the proposed framework is demonstrated through a case study involving a university department's collaboration network, where relationships between faculty members are analyzed using data collected via a fascinating mouse-tracking technique.

这项研究的动机源于人类社会互动中固有的复杂性和模糊性，而传统的社会网络分析（SNA）方法往往无法充分捕捉到这一点。传统的 SNA 方法通常用二元或加权纽带来表示关系，从而忽略了现实世界中社会联系的细微差别和内在不确定性。由于需要保留社会关系的模糊性，并对这些关系提供更准确的表述，这就促使人们在 SNA 中引入基于模糊的方法。本文提出了一个新颖的模糊社会网络分析（FSNA）框架，它扩展了传统的 SNA，以适应关系的模糊性。所提出的方法将节点之间的联系重新定义为模糊数而非清晰值，并引入了一套完整的模糊中心性指数，包括模糊度中心性、模糊度间中心性和模糊接近中心性等。这些指数旨在衡量网络中节点的重要性和影响力，同时保留节点之间关系的不确定性。我们通过一个涉及大学院系合作网络的案例研究来证明所提出的框架的适用性，在该案例研究中，我们利用迷人的鼠标跟踪技术收集的数据分析了教师之间的关系。

{"title":"Fuzzy centrality measures in social network analysis: Theory and application in a university department collaboration network","authors":"Annamaria Porreca , Fabrizio Maturo , Viviana Ventre","doi":"10.1016/j.ijar.2024.109319","DOIUrl":"10.1016/j.ijar.2024.109319","url":null,"abstract":"<div><div>The motivation behind this research stems from the inherent complexity and vagueness in human social interactions, which traditional Social Network Analysis (SNA) approaches often fail to capture adequately. Conventional SNA methods typically represent relationships as binary or weighted ties, thereby losing the subtle nuances and inherent uncertainty in real-world social connections. The need to preserve the vagueness of social relations and provide a more accurate representation of these relationships motivates the introduction of a fuzzy-based approach to SNA. This paper proposes a novel framework for Fuzzy Social Network Analysis (FSNA), which extends traditional SNA to accommodate the vagueness of relationships. The proposed method redefines the ties between nodes as fuzzy numbers rather than crisp values and introduces a comprehensive set of fuzzy centrality indices, including fuzzy degree centrality, fuzzy betweenness centrality, and fuzzy closeness centrality, among others. These indices are designed to measure the importance and influence of nodes within a network while preserving the uncertainty in the relationships between them. The applicability of the proposed framework is demonstrated through a case study involving a university department's collaboration network, where relationships between faculty members are analyzed using data collected via a fascinating mouse-tracking technique.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109319"},"PeriodicalIF":3.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anomaly detection based on improved k-nearest neighbor rough sets 基于改进的 k 近邻粗糙集的异常检测

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2024-11-13 DOI: 10.1016/j.ijar.2024.109323

Xiwen Chen , Zhong Yuan , Shan Feng

Neighborhood rough set model is a resultful model for processing incomplete, imprecise, and other uncertain data. It has been used in several fields, such as anomaly detection and data classification. However, most of the current neighborhood rough set models suffer from the issues of unreasonable neighborhood radius determination and poor adaptability. To obtain an adaptive neighborhood radius and make granulation results more reasonable, an improved k-nearest neighbor rough set model is proposed in the paper by introducing kth-distance as the k-nearest neighborhood radius, and an anomaly detection model is constructed. In the method, the k-nearest neighborhood radius is used to calculate the k-nearest neighbor relation firstly. Then, the anomaly degree of granule (GAD) is defined to measure the anomaly degree of k-nearest neighbor granules by combining approximation accuracy with the local density. Furthermore, the GADs of an object's k-nearest neighbor granules generated by different attribute subsets are calculated, and the anomaly score (AS) is constructed. Finally, an anomaly detection algorithm is designed. Some mainstream anomaly detection methods are compared with the proposed method on public datasets. The results indicate that the capability of detecting anomalies of the proposed approach outperforms current detection methods.

邻域粗糙集模型是一种处理不完整、不精确和其他不确定数据的有效模型。它已被用于异常检测和数据分类等多个领域。然而，目前大多数邻域粗糙集模型都存在邻域半径确定不合理、适应性差等问题。为了获得自适应的邻域半径，使粒化结果更加合理，本文提出了一种改进的 k 近邻粗糙集模型，引入第 k 次距离作为 k 近邻半径，并构建了异常检测模型。在该方法中，首先利用 k 近邻半径计算 k 近邻关系。然后定义颗粒异常度（GAD），通过结合近似精度和局部密度来衡量 k 近邻颗粒的异常度。此外，还计算了由不同属性子集生成的对象 k 近邻颗粒的 GAD，并构建了异常得分（AS）。最后，设计异常检测算法。在公共数据集上，将一些主流异常检测方法与所提出的方法进行了比较。结果表明，所提方法的异常检测能力优于当前的检测方法。

{"title":"Anomaly detection based on improved k-nearest neighbor rough sets","authors":"Xiwen Chen , Zhong Yuan , Shan Feng","doi":"10.1016/j.ijar.2024.109323","DOIUrl":"10.1016/j.ijar.2024.109323","url":null,"abstract":"<div><div>Neighborhood rough set model is a resultful model for processing incomplete, imprecise, and other uncertain data. It has been used in several fields, such as anomaly detection and data classification. However, most of the current neighborhood rough set models suffer from the issues of unreasonable neighborhood radius determination and poor adaptability. To obtain an adaptive neighborhood radius and make granulation results more reasonable, an improved <em>k</em>-nearest neighbor rough set model is proposed in the paper by introducing <em>k</em>th-distance as the <em>k</em>-nearest neighborhood radius, and an anomaly detection model is constructed. In the method, the <em>k</em>-nearest neighborhood radius is used to calculate the <em>k</em>-nearest neighbor relation firstly. Then, the anomaly degree of granule (GAD) is defined to measure the anomaly degree of <em>k</em>-nearest neighbor granules by combining approximation accuracy with the local density. Furthermore, the GADs of an object's <em>k</em>-nearest neighbor granules generated by different attribute subsets are calculated, and the anomaly score (AS) is constructed. Finally, an anomaly detection algorithm is designed. Some mainstream anomaly detection methods are compared with the proposed method on public datasets. The results indicate that the capability of detecting anomalies of the proposed approach outperforms current detection methods.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109323"},"PeriodicalIF":3.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inner product reduction for fuzzy formal contexts 模糊形式语境的内积还原

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2024-11-08 DOI: 10.1016/j.ijar.2024.109324

Qing Wang , Xiuwei Gao

Formal concept analysis finds application across multiple domains, including knowledge representation, data mining, and decision analysis. Within this framework, the exploration of attribute reduction for fuzzy formal contexts represents a substantial area of research. We introduce a novel form of attribute reduction for fuzzy formal contexts named inner product reduction, and an algorithm for finding all inner product reducts is given by using the indiscernibility matrix, and a calculation example is given. Furthermore, for consistent fuzzy decision formal contexts, the definition and algorithm of inner product reduction are given. Finally, the concept and algorithm of inner product reduction are extended to general fuzzy decision formal contexts. Through experimental verification, the viability and efficacy of the inner product reduction algorithm for fuzzy formal contexts are verified.

形式概念分析应用于多个领域，包括知识表示、数据挖掘和决策分析。在这一框架内，探索模糊形式语境的属性还原是一个重要的研究领域。我们为模糊形式语境引入了一种名为 "内积还原 "的新型属性还原形式，并利用不可辨矩阵给出了一种查找所有内积还原的算法，还给出了一个计算实例。此外，针对一致模糊决策形式语境，给出了内积还原的定义和算法。最后，将内积还原的概念和算法扩展到一般模糊决策形式语境。通过实验验证了模糊形式语境内积缩减算法的可行性和有效性。

引用次数: 0

Sequential merging and construction of rankings as cognitive logic 作为认知逻辑的序列合并和排名构建

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2024-11-08 DOI: 10.1016/j.ijar.2024.109321

Kai Sauerwald , Eda Ismail-Tsaous , Marco Ragni , Gabriele Kern-Isberner , Christoph Beierle

We introduce and evaluate a cognitively inspired formal reasoning approach that sequentially applies a combination of a belief merging operator and a ranking construction operator. The approach is inspired by human propositional reasoning, which is understood here as a sequential process in which the agent constructs a new epistemic state in each task step according to newly acquired information. Formally, we model epistemic states by Spohn's ranking functions. The posterior representation of the epistemic state is obtained by merging the prior ranking function and a ranking function constructed from the new piece of information. We denote this setup as the sequential merging approach. The approach abstracts from the concrete merging operation and abstracts from the concrete way of constructing a ranking function according to new information. We formally show that sequential merging is capable of predicting with theoretical maximum achievable accuracy. Various instantiations of our approach are benchmarked on data from a psychological experiment, demonstrating that sequential merging provides formal reasoning methods that are cognitively more adequate than classical logic.

我们介绍并评估了一种受认知启发的形式推理方法，它依次应用信念合并算子和排序构建算子的组合。这种方法受到人类命题推理的启发，在这里，命题推理被理解为一个连续的过程，在这个过程中，代理在每个任务步骤中根据新获得的信息构建一个新的认识状态。从形式上讲，我们用斯波恩排序函数来模拟认识状态。认识状态的后置表示是通过合并先前的排序函数和根据新信息构建的排序函数而得到的。我们将这种设置称为顺序合并法。这种方法抽象了具体的合并操作，也抽象了根据新信息构建排序函数的具体方法。我们从形式上证明，顺序合并能够以理论上可达到的最高准确率进行预测。我们根据心理实验数据对我们方法的各种实例进行了基准测试，证明顺序合并提供的形式推理方法在认知上比经典逻辑更充分。

{"title":"Sequential merging and construction of rankings as cognitive logic","authors":"Kai Sauerwald , Eda Ismail-Tsaous , Marco Ragni , Gabriele Kern-Isberner , Christoph Beierle","doi":"10.1016/j.ijar.2024.109321","DOIUrl":"10.1016/j.ijar.2024.109321","url":null,"abstract":"<div><div>We introduce and evaluate a cognitively inspired formal reasoning approach that sequentially applies a combination of a belief merging operator and a ranking construction operator. The approach is inspired by human propositional reasoning, which is understood here as a sequential process in which the agent constructs a new epistemic state in each task step according to newly acquired information. Formally, we model epistemic states by Spohn's ranking functions. The posterior representation of the epistemic state is obtained by merging the prior ranking function and a ranking function constructed from the new piece of information. We denote this setup as the sequential merging approach. The approach abstracts from the concrete merging operation and abstracts from the concrete way of constructing a ranking function according to new information. We formally show that sequential merging is capable of predicting with theoretical maximum achievable accuracy. Various instantiations of our approach are benchmarked on data from a psychological experiment, demonstrating that sequential merging provides formal reasoning methods that are cognitively more adequate than classical logic.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109321"},"PeriodicalIF":3.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-sample means comparisons for imprecise interval data 不精确区间数据的多样本均值比较

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2024-11-08 DOI: 10.1016/j.ijar.2024.109322

Yan Sun , Zac Rios , Brennan Bean

In recent years, interval data have become an increasingly popular tool to solving modern data problems. Intervals are now often used for dimensionality reduction, data aggregation, privacy censorship, and quantifying awareness of various uncertainties. Among many statistical methods that are being studied and developed for interval data, significance tests are of particular importance due to their fundamental value both in theory and practice. The difficulty in developing such tests mainly lies in the fact that the concept of normality does not extend naturally to intervals, making the exact tests hard to formulate. As a result, most existing works have relied on bootstrap methods to approximate null distributions. However, this is not always feasible given limited sample sizes or other intrinsic characteristics of the data. In this paper, we propose a novel asymptotic test for comparing multi-sample means with interval data as a generalization of the classic ANOVA. Based on the random sets theory, we construct the test statistic in the form of a ratio of between-group interval variance and within-group interval variance. The limiting null distribution is derived under usual assumptions and mild regularity conditions. Simulation studies with various data configurations validate the asymptotic result, and show promising small sample performances. Finally, a real interval data ANOVA analysis is presented that showcases the applicability of our method.

近年来，区间数据日益成为解决现代数据问题的常用工具。区间数据现在经常被用于降维、数据聚合、隐私审查以及量化对各种不确定性的认识。在针对区间数据研究和开发的众多统计方法中，显著性检验因其在理论和实践中的基本价值而尤为重要。开发这类检验的难点主要在于，正态性的概念并不能自然地延伸到区间，因此很难制定精确的检验方法。因此，大多数现有研究都依赖于引导法来近似空分布。然而，考虑到有限的样本量或数据的其他固有特征，这种方法并不总是可行的。在本文中，我们提出了一种新的渐近检验方法，用于比较区间数据的多样本均值，作为经典方差分析的一般化。基于随机集理论，我们以组间区间方差和组内区间方差之比形式构建检验统计量。在通常的假设和温和的正则条件下，推导出了极限零分布。利用各种数据配置进行的模拟研究验证了渐近结果，并显示出良好的小样本性能。最后，介绍了一个真实的区间数据方差分析，展示了我们方法的适用性。

{"title":"Multi-sample means comparisons for imprecise interval data","authors":"Yan Sun , Zac Rios , Brennan Bean","doi":"10.1016/j.ijar.2024.109322","DOIUrl":"10.1016/j.ijar.2024.109322","url":null,"abstract":"<div><div>In recent years, interval data have become an increasingly popular tool to solving modern data problems. Intervals are now often used for dimensionality reduction, data aggregation, privacy censorship, and quantifying awareness of various uncertainties. Among many statistical methods that are being studied and developed for interval data, significance tests are of particular importance due to their fundamental value both in theory and practice. The difficulty in developing such tests mainly lies in the fact that the concept of normality does not extend naturally to intervals, making the exact tests hard to formulate. As a result, most existing works have relied on bootstrap methods to approximate null distributions. However, this is not always feasible given limited sample sizes or other intrinsic characteristics of the data. In this paper, we propose a novel asymptotic test for comparing multi-sample means with interval data as a generalization of the classic ANOVA. Based on the random sets theory, we construct the test statistic in the form of a ratio of between-group interval variance and within-group interval variance. The limiting null distribution is derived under usual assumptions and mild regularity conditions. Simulation studies with various data configurations validate the asymptotic result, and show promising small sample performances. Finally, a real interval data ANOVA analysis is presented that showcases the applicability of our method.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109322"},"PeriodicalIF":3.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A robust multi-label feature selection based on label significance and fuzzy entropy 基于标签显著性和模糊熵的鲁棒多标签特征选择

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2024-11-07 DOI: 10.1016/j.ijar.2024.109310

Taoli Yang , Changzhong Wang , Yiying Chen , Tingquan Deng

Multi-label feature selection is one of the key steps in dealing with multi-label classification problems in high-dimensional data. In this step, label enhancement techniques play an important role. However, it is worth noting that many current methods tend to ignore the intrinsic connection between inter-sample similarity and inter-label correlation when implementing label enhancement learning. The neglect may prevent the process of label enhancement from accurately revealing the complex structure and underlying patterns within data. For this reason, a fuzzy multi-label feature selection method based on label significance and fuzzy entropy is proposed. An innovative label enhancement technique that considers not only the intrinsic connection between features and labels, but also the correlation between labels was first devised. Based on this enhanced label representation, the concept of fuzzy entropy is further defined to quantify the uncertainty of features for multi-label classification tasks. Subsequently, a feature selection algorithm based on feature importance and label importance was developed. The algorithm guides the feature selection process by evaluating how much each feature contributes to multi-label classification and how important each label is to the overall classification task. Finally, through a series of experimental validation, the proposed algorithm is proved to have better performance for multi-label classification tasks.

多标签特征选择是处理高维数据中多标签分类问题的关键步骤之一。在这一步骤中，标签增强技术发挥着重要作用。然而，值得注意的是，当前许多方法在实施标签增强学习时，往往会忽略样本间相似性和标签间相关性之间的内在联系。这种忽视可能会导致标签增强过程无法准确揭示数据的复杂结构和潜在模式。为此，我们提出了一种基于标签重要性和模糊熵的模糊多标签特征选择方法。首先设计了一种创新的标签增强技术，它不仅考虑了特征与标签之间的内在联系，还考虑了标签之间的相关性。在这种增强标签表示法的基础上，进一步定义了模糊熵的概念，以量化多标签分类任务中特征的不确定性。随后，开发了一种基于特征重要性和标签重要性的特征选择算法。该算法通过评估每个特征对多标签分类的贡献程度以及每个标签对整个分类任务的重要性来指导特征选择过程。最后，通过一系列实验验证，证明所提出的算法在多标签分类任务中具有更好的性能。

{"title":"A robust multi-label feature selection based on label significance and fuzzy entropy","authors":"Taoli Yang , Changzhong Wang , Yiying Chen , Tingquan Deng","doi":"10.1016/j.ijar.2024.109310","DOIUrl":"10.1016/j.ijar.2024.109310","url":null,"abstract":"<div><div>Multi-label feature selection is one of the key steps in dealing with multi-label classification problems in high-dimensional data. In this step, label enhancement techniques play an important role. However, it is worth noting that many current methods tend to ignore the intrinsic connection between inter-sample similarity and inter-label correlation when implementing label enhancement learning. The neglect may prevent the process of label enhancement from accurately revealing the complex structure and underlying patterns within data. For this reason, a fuzzy multi-label feature selection method based on label significance and fuzzy entropy is proposed. An innovative label enhancement technique that considers not only the intrinsic connection between features and labels, but also the correlation between labels was first devised. Based on this enhanced label representation, the concept of fuzzy entropy is further defined to quantify the uncertainty of features for multi-label classification tasks. Subsequently, a feature selection algorithm based on feature importance and label importance was developed. The algorithm guides the feature selection process by evaluating how much each feature contributes to multi-label classification and how important each label is to the overall classification task. Finally, through a series of experimental validation, the proposed algorithm is proved to have better performance for multi-label classification tasks.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109310"},"PeriodicalIF":3.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-label feature selection based on adaptive label enhancement and class-imbalance-aware fuzzy information entropy 基于自适应标签增强和类不平衡感知模糊信息熵的多标签特征选择

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2024-11-06 DOI: 10.1016/j.ijar.2024.109320

Qiong Liu , Mingjie Cai , Qingguo Li , Chaoqun Huang

Multi-label feature selection can select representative features to reduce the dimension of data. Since existing multi-label feature selection methods usually suppose that the significance of all labels is consistent, the relationships between samples in the entire label space are generated straightforwardly such that the shape of label distribution and the property of class-imbalance are ignored. To address these issues, we propose a novel multi-label feature selection approach. Based on non-negative matrix factorization (NMF), the similarities between the logical label and label distribution are constrained, which ensures that the shape of label distribution does not deviate from the underlying actual shape to some extent. Further, the relationships between samples in label space and feature space are restricted by graph embedding. Finally, we leverage the properties of label distribution and class-imbalance to generate the relationships between samples in label space and propose a multi-label feature selection approach based on fuzzy information entropy. Eight state-of-the-art methods are compared with the proposed method to validate the effectiveness of our method.

多标签特征选择可以选择具有代表性的特征，从而降低数据维度。由于现有的多标签特征选择方法通常假定所有标签的重要性是一致的，因此整个标签空间中样本之间的关系是直接生成的，从而忽略了标签分布的形状和类不平衡的特性。为了解决这些问题，我们提出了一种新颖的多标签特征选择方法。基于非负矩阵因式分解（NMF），逻辑标签和标签分布之间的相似性受到了约束，从而确保标签分布的形状不会在一定程度上偏离基本的实际形状。此外，标签空间和特征空间中样本之间的关系受到图嵌入的限制。最后，我们利用标签分布和类不平衡的特性来生成标签空间中样本之间的关系，并提出了一种基于模糊信息熵的多标签特征选择方法。我们将八种最先进的方法与所提出的方法进行了比较，以验证我们方法的有效性。

{"title":"Multi-label feature selection based on adaptive label enhancement and class-imbalance-aware fuzzy information entropy","authors":"Qiong Liu , Mingjie Cai , Qingguo Li , Chaoqun Huang","doi":"10.1016/j.ijar.2024.109320","DOIUrl":"10.1016/j.ijar.2024.109320","url":null,"abstract":"<div><div>Multi-label feature selection can select representative features to reduce the dimension of data. Since existing multi-label feature selection methods usually suppose that the significance of all labels is consistent, the relationships between samples in the entire label space are generated straightforwardly such that the shape of label distribution and the property of class-imbalance are ignored. To address these issues, we propose a novel multi-label feature selection approach. Based on non-negative matrix factorization (NMF), the similarities between the logical label and label distribution are constrained, which ensures that the shape of label distribution does not deviate from the underlying actual shape to some extent. Further, the relationships between samples in label space and feature space are restricted by graph embedding. Finally, we leverage the properties of label distribution and class-imbalance to generate the relationships between samples in label space and propose a multi-label feature selection approach based on fuzzy information entropy. Eight state-of-the-art methods are compared with the proposed method to validate the effectiveness of our method.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109320"},"PeriodicalIF":3.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A three-way decision combining multi-granularity variable precision fuzzy rough set and TOPSIS method 多粒度可变精度模糊粗糙集与 TOPSIS 法相结合的三向决策

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2024-11-04 DOI: 10.1016/j.ijar.2024.109318

Chengzhao Jia, Lingqiang Li, Xinru Li

This study proposed an innovative fuzzy rough set model to address multi-attribute decision-making problems. Initially, we introduced a novel model of multi-granularity variable precision fuzzy rough sets, which included three foundational models. This model was demonstrated to possess favorable algebraic and topological properties, and particularly noteworthy the comparable property. Subsequently, by integrating the novel model with the TOPSIS method, a novel three-way decision model was proposed. Within this framework, three fundamental models of multi-granularity variable precision fuzzy rough sets were applied in three methods to construct relative loss functions. This resulted in a three-way decision model with three distinct strategies. Finally, we implemented the proposed three-way decision model for risk detection in maternal women. Several experiments and comparisons were conducted to validate the effectiveness, stability, and reliability of our proposed approach. The experimental results indicated that the proposed method accurately classified and ranked maternal women. Overall, our approach offered multiple strategies and fault tolerance and was found to be effective for a large amount of data.

本研究提出了一种创新的模糊粗糙集模型来解决多属性决策问题。首先，我们介绍了一种新颖的多粒度变精度模糊粗糙集模型，其中包括三个基础模型。研究证明，该模型具有良好的代数和拓扑特性，尤其值得注意的是其可比性。随后，通过将新模型与 TOPSIS 方法相结合，提出了一种新的三向决策模型。在此框架内，多粒度变精度模糊粗糙集的三个基本模型被应用于三种方法来构建相对损失函数。这就产生了具有三种不同策略的三向决策模型。最后，我们将提出的三向决策模型用于产妇风险检测。为了验证我们提出的方法的有效性、稳定性和可靠性，我们进行了多次实验和比较。实验结果表明，所提出的方法准确地对产妇进行了分类和排序。总体而言，我们的方法提供了多种策略和容错功能，并且对大量数据有效。

{"title":"A three-way decision combining multi-granularity variable precision fuzzy rough set and TOPSIS method","authors":"Chengzhao Jia, Lingqiang Li, Xinru Li","doi":"10.1016/j.ijar.2024.109318","DOIUrl":"10.1016/j.ijar.2024.109318","url":null,"abstract":"<div><div>This study proposed an innovative fuzzy rough set model to address multi-attribute decision-making problems. Initially, we introduced a novel model of multi-granularity variable precision fuzzy rough sets, which included three foundational models. This model was demonstrated to possess favorable algebraic and topological properties, and particularly noteworthy the comparable property. Subsequently, by integrating the novel model with the TOPSIS method, a novel three-way decision model was proposed. Within this framework, three fundamental models of multi-granularity variable precision fuzzy rough sets were applied in three methods to construct relative loss functions. This resulted in a three-way decision model with three distinct strategies. Finally, we implemented the proposed three-way decision model for risk detection in maternal women. Several experiments and comparisons were conducted to validate the effectiveness, stability, and reliability of our proposed approach. The experimental results indicated that the proposed method accurately classified and ranked maternal women. Overall, our approach offered multiple strategies and fault tolerance and was found to be effective for a large amount of data.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"176 ","pages":"Article 109318"},"PeriodicalIF":3.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rough sets, modal logic and approximate reasoning 粗糙集、模态逻辑和近似推理

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning

Pub Date : 2024-11-04 DOI: 10.1016/j.ijar.2024.109305

Mihir Kr. Chakraborty , Sandip Majumder , Samarjit Kar

This paper introduces an approximate reasoning method based on rough sets and modal logic. Various Approximate Modus Ponens rules are investigated and defined in Modal Logic systems interpreted in the rough set language. Although this is primarily theoretical work, we expect natural applications of the technique in real-life scenarios. An attempt in this direction is made in a real case analysis to logically model some issues of legal interest.

本文介绍了一种基于粗糙集和模态逻辑的近似推理方法。在用粗糙集语言解释的模态逻辑系统中研究和定义了各种近似模态规则。虽然这主要是理论工作，但我们期望该技术在现实生活中得到自然应用。我们在实际案例分析中进行了这方面的尝试，对一些法律问题进行了逻辑建模。

引用次数: 0